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When Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it would soon grow into a monster hurricane.

As the primary meteorologist on duty, he forecasted that in just 24 hours the storm would become a category 4 hurricane and start shifting towards the coast of Jamaica. Not a single expert had ever issued this confident prediction for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s new DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his confidence: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a most intense storm. Although I am unprepared to forecast that intensity yet given track uncertainty, that is still plausible.

“There is a high probability that a period of rapid intensification is expected as the system moves slowly over very warm sea temperatures which represent the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

The AI model is the pioneer artificial intelligence system dedicated to tropical cyclones, and now the initial to beat standard weather forecasters at their own game. Through all tropical systems so far this year, Google’s model is the best – surpassing experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful coastal impacts recorded in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents additional preparation time to prepare for the disaster, possibly saving lives and property.

How Google’s Model Works

The AI system operates through identifying trends that conventional lengthy physics-based weather models may miss.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in certain instances, more accurate than the slower traditional weather models we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

To be sure, the system is an instance of machine learning – a method that has been employed in research fields like weather science for a long time – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a manner that its system only requires minutes to come up with an result, and can operate on a standard PC – in strong contrast to the primary systems that authorities have used for years that can take hours to run and require the largest high-performance systems in the world.

Expert Responses and Future Advances

Nevertheless, the reality that Google’s model could exceed previous gold-standard legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense storms.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is now large enough that it’s pretty clear this is not just chance.”

He said that although Google DeepMind is outperforming all other models on predicting the trajectory of hurricanes worldwide this year, like many AI models it sometimes errs on extreme strength predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, he said he plans to discuss with Google about how it can enhance the AI results more useful for forecasters by providing additional under-the-hood data they can use to evaluate exactly why it is coming up with its answers.

“A key concern that nags at me is that although these forecasts seem to be highly accurate, the results of the system is kind of a opaque process,” remarked Franklin.

Wider Sector Trends

There has never been a commercial entity that has developed a high-performance forecasting system which allows researchers a view of its methods – in contrast to nearly all systems which are offered free to the public in their full form by the authorities that created and operate them.

Google is not alone in starting to use artificial intelligence to solve challenging weather forecasting problems. The authorities also have their respective artificial intelligence systems in the development phase – which have also shown better performance over previous non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to do so. One company, WindBorne Systems, is also deploying its own atmospheric sensors to fill the gaps in the US weather-observing network.